Overview

Dataset statistics

Number of variables15
Number of observations390
Missing cells23
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.7 KiB
Average record size in memory107.0 B

Variable types

Numeric13
Categorical2

Alerts

BMI is highly overall correlated with Weight and 2 other fieldsHigh correlation
CLASS is highly overall correlated with glyhb and 1 other fieldsHigh correlation
Diastolic_Blood_Pressure is highly overall correlated with Systolic_Blood_PressureHigh correlation
Gender is highly overall correlated with HeightHigh correlation
Height is highly overall correlated with GenderHigh correlation
Systolic_Blood_Pressure is highly overall correlated with Diastolic_Blood_PressureHigh correlation
Weight is highly overall correlated with BMI and 2 other fieldsHigh correlation
glyhb is highly overall correlated with CLASS and 1 other fieldsHigh correlation
hdl is highly overall correlated with ratioHigh correlation
hip is highly overall correlated with BMI and 2 other fieldsHigh correlation
ratio is highly overall correlated with hdlHigh correlation
stab.glu is highly overall correlated with CLASS and 1 other fieldsHigh correlation
waist is highly overall correlated with BMI and 2 other fieldsHigh correlation
Systolic_Blood_Pressure has 5 (1.3%) missing valuesMissing
Diastolic_Blood_Pressure has 5 (1.3%) missing valuesMissing

Reproduction

Analysis started2024-04-12 18:10:21.398861
Analysis finished2024-04-12 18:11:10.633016
Duration49.23 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

chol
Real number (ℝ)

Distinct153
Distinct (%)39.3%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean207.27506
Minimum78
Maximum443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:10.841981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile144.4
Q1179
median203
Q3229
95-th percentile290.8
Maximum443
Range365
Interquartile range (IQR)50

Descriptive statistics

Standard deviation44.71495
Coefficient of variation (CV)0.21572759
Kurtosis2.6657693
Mean207.27506
Median Absolute Deviation (MAD)25
Skewness0.9582663
Sum80630
Variance1999.4267
MonotonicityNot monotonic
2024-04-12T18:11:11.153227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 11
 
2.8%
204 9
 
2.3%
194 7
 
1.8%
219 7
 
1.8%
215 7
 
1.8%
199 6
 
1.5%
203 6
 
1.5%
181 5
 
1.3%
209 5
 
1.3%
173 5
 
1.3%
Other values (143) 321
82.3%
ValueCountFrequency (%)
78 1
0.3%
115 1
0.3%
118 1
0.3%
122 1
0.3%
128 1
0.3%
129 1
0.3%
132 2
0.5%
134 2
0.5%
135 2
0.5%
136 1
0.3%
ValueCountFrequency (%)
443 1
0.3%
404 1
0.3%
347 1
0.3%
342 1
0.3%
337 1
0.3%
322 1
0.3%
318 1
0.3%
307 1
0.3%
306 1
0.3%
305 1
0.3%

stab.glu
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.33846
Minimum48
Maximum385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:11.466864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile68
Q181
median90
Q3107.75
95-th percentile234.1
Maximum385
Range337
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation53.798188
Coefficient of variation (CV)0.50120141
Kurtosis7.9059129
Mean107.33846
Median Absolute Deviation (MAD)12
Skewness2.7111213
Sum41862
Variance2894.245
MonotonicityNot monotonic
2024-04-12T18:11:11.750603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 18
 
4.6%
81 15
 
3.8%
92 14
 
3.6%
84 12
 
3.1%
87 12
 
3.1%
77 11
 
2.8%
83 11
 
2.8%
82 10
 
2.6%
76 10
 
2.6%
74 10
 
2.6%
Other values (106) 267
68.5%
ValueCountFrequency (%)
48 1
0.3%
52 1
0.3%
54 1
0.3%
56 2
0.5%
57 1
0.3%
58 1
0.3%
59 1
0.3%
60 1
0.3%
62 1
0.3%
64 2
0.5%
ValueCountFrequency (%)
385 1
0.3%
371 1
0.3%
369 1
0.3%
342 1
0.3%
341 1
0.3%
330 1
0.3%
299 1
0.3%
297 1
0.3%
279 1
0.3%
270 2
0.5%

hdl
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)19.3%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean50.267352
Minimum12
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:12.086441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile29.4
Q138
median46
Q359
95-th percentile85.6
Maximum120
Range108
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.301317
Coefficient of variation (CV)0.34418595
Kurtosis2.109795
Mean50.267352
Median Absolute Deviation (MAD)10
Skewness1.2272745
Sum19554
Variance299.33556
MonotonicityNot monotonic
2024-04-12T18:11:12.387980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 21
 
5.4%
44 20
 
5.1%
36 20
 
5.1%
34 18
 
4.6%
42 15
 
3.8%
40 14
 
3.6%
37 11
 
2.8%
54 11
 
2.8%
58 9
 
2.3%
48 9
 
2.3%
Other values (65) 241
61.8%
ValueCountFrequency (%)
12 1
 
0.3%
14 1
 
0.3%
23 1
 
0.3%
24 5
1.3%
25 1
 
0.3%
26 3
0.8%
28 4
1.0%
29 4
1.0%
30 5
1.3%
31 5
1.3%
ValueCountFrequency (%)
120 1
 
0.3%
118 1
 
0.3%
117 1
 
0.3%
114 1
 
0.3%
110 1
 
0.3%
108 1
 
0.3%
100 1
 
0.3%
94 1
 
0.3%
92 4
1.0%
91 1
 
0.3%

ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)17.7%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean4.5264781
Minimum1.5
Maximum19.299999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:12.805187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.5
Q13.2
median4.1999998
Q35.4000001
95-th percentile7.3000002
Maximum19.299999
Range17.799999
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.7384799
Coefficient of variation (CV)0.384069
Kurtosis13.522397
Mean4.5264781
Median Absolute Deviation (MAD)1.0999999
Skewness2.2409389
Sum1760.8
Variance3.0223125
MonotonicityNot monotonic
2024-04-12T18:11:13.256368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.599999905 15
 
3.8%
5.300000191 14
 
3.6%
3 14
 
3.6%
3.099999905 13
 
3.3%
2.900000095 12
 
3.1%
4.300000191 12
 
3.1%
4.099999905 12
 
3.1%
3.299999952 11
 
2.8%
3.900000095 11
 
2.8%
5.099999905 11
 
2.8%
Other values (59) 264
67.7%
ValueCountFrequency (%)
1.5 1
 
0.3%
1.899999976 1
 
0.3%
2 1
 
0.3%
2.099999905 1
 
0.3%
2.200000048 4
 
1.0%
2.299999952 2
 
0.5%
2.400000095 6
1.5%
2.5 6
1.5%
2.599999905 10
2.6%
2.700000048 7
1.8%
ValueCountFrequency (%)
19.29999924 1
0.3%
12.19999981 1
0.3%
10.60000038 1
0.3%
9.399999619 1
0.3%
8.899999619 2
0.5%
8.699999809 1
0.3%
8.300000191 1
0.3%
8 1
0.3%
7.900000095 2
0.5%
7.800000191 2
0.5%

glyhb
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5897692
Minimum2.6800001
Maximum16.110001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:13.649357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.6800001
5-th percentile3.75
Q14.3800001
median4.8400002
Q35.5999999
95-th percentile10.9165
Maximum16.110001
Range13.430001
Interquartile range (IQR)1.2199998

Descriptive statistics

Standard deviation2.2425948
Coefficient of variation (CV)0.40119632
Kurtosis5.1064866
Mean5.5897692
Median Absolute Deviation (MAD)0.55999994
Skewness2.2461247
Sum2180.01
Variance5.0292316
MonotonicityNot monotonic
2024-04-12T18:11:14.057156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.670000076 6
 
1.5%
4.400000095 6
 
1.5%
4.309999943 5
 
1.3%
4.409999847 5
 
1.3%
5.349999905 5
 
1.3%
4.659999847 5
 
1.3%
5.230000019 4
 
1.0%
4.610000134 4
 
1.0%
4.949999809 4
 
1.0%
4.380000114 4
 
1.0%
Other values (229) 342
87.7%
ValueCountFrequency (%)
2.680000067 1
0.3%
2.730000019 1
0.3%
2.849999905 2
0.5%
3.029999971 1
0.3%
3.329999924 1
0.3%
3.410000086 1
0.3%
3.440000057 1
0.3%
3.549999952 2
0.5%
3.559999943 1
0.3%
3.579999924 1
0.3%
ValueCountFrequency (%)
16.11000061 1
0.3%
15.52000046 1
0.3%
14.93999958 1
0.3%
14.31000042 1
0.3%
13.69999981 1
0.3%
13.63000011 1
0.3%
13.60000038 1
0.3%
13.06000042 1
0.3%
12.97000027 1
0.3%
12.73999977 1
0.3%

Age
Real number (ℝ)

Distinct68
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.774359
Minimum19
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:14.454120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q134
median44.5
Q360
95-th percentile76
Maximum92
Range73
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.435911
Coefficient of variation (CV)0.35138721
Kurtosis-0.6631611
Mean46.774359
Median Absolute Deviation (MAD)13.5
Skewness0.33290667
Sum18242
Variance270.13919
MonotonicityNot monotonic
2024-04-12T18:11:14.868269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 16
 
4.1%
36 13
 
3.3%
41 12
 
3.1%
43 12
 
3.1%
37 11
 
2.8%
38 11
 
2.8%
63 11
 
2.8%
60 10
 
2.6%
50 10
 
2.6%
20 10
 
2.6%
Other values (58) 274
70.3%
ValueCountFrequency (%)
19 2
 
0.5%
20 10
2.6%
21 6
1.5%
22 5
1.3%
23 7
1.8%
24 1
 
0.3%
25 4
 
1.0%
26 6
1.5%
27 9
2.3%
28 8
2.1%
ValueCountFrequency (%)
92 1
 
0.3%
91 1
 
0.3%
89 1
 
0.3%
84 1
 
0.3%
83 1
 
0.3%
82 2
0.5%
81 1
 
0.3%
80 2
0.5%
79 2
0.5%
78 4
1.0%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size642.0 B
female
228 
male
162 

Length

Max length6
Median length6
Mean length5.1692308
Min length4

Characters and Unicode

Total characters2016
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 228
58.5%
male 162
41.5%

Length

2024-04-12T18:11:15.361647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T18:11:15.824743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 228
58.5%
male 162
41.5%

Most occurring characters

ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Height
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)5.7%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean65.979328
Minimum52
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:16.020946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60
Q163
median66
Q369
95-th percentile72
Maximum76
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9267366
Coefficient of variation (CV)0.05951465
Kurtosis-0.21097518
Mean65.979328
Median Absolute Deviation (MAD)3
Skewness0.016563487
Sum25534
Variance15.419261
MonotonicityNot monotonic
2024-04-12T18:11:16.285922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
63 40
10.3%
69 37
9.5%
67 36
9.2%
65 33
8.5%
62 32
8.2%
64 32
8.2%
66 31
7.9%
68 26
 
6.7%
70 22
 
5.6%
71 21
 
5.4%
Other values (12) 77
19.7%
ValueCountFrequency (%)
52 1
 
0.3%
55 1
 
0.3%
56 1
 
0.3%
58 3
 
0.8%
59 9
 
2.3%
60 10
 
2.6%
61 20
5.1%
62 32
8.2%
63 40
10.3%
64 32
8.2%
ValueCountFrequency (%)
76 2
 
0.5%
75 3
 
0.8%
74 5
 
1.3%
73 8
 
2.1%
72 14
 
3.6%
71 21
5.4%
70 22
5.6%
69 37
9.5%
68 26
6.7%
67 36
9.2%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.20256
Minimum103.5
Maximum322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:16.548987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum103.5
5-th percentile119.45
Q1149.5
median172.25
Q3196.75
95-th percentile250.55
Maximum322
Range218.5
Interquartile range (IQR)47.25

Descriptive statistics

Standard deviation39.300863
Coefficient of variation (CV)0.22304365
Kurtosis0.84381342
Mean176.20256
Median Absolute Deviation (MAD)23.75
Skewness0.77717299
Sum68719
Variance1544.5578
MonotonicityNot monotonic
2024-04-12T18:11:16.830708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.5 8
 
2.1%
165 6
 
1.5%
120 5
 
1.3%
168.5 5
 
1.3%
163 5
 
1.3%
199 5
 
1.3%
180 5
 
1.3%
159 5
 
1.3%
185 5
 
1.3%
183 5
 
1.3%
Other values (193) 336
86.2%
ValueCountFrequency (%)
103.5 1
 
0.3%
104.5 1
 
0.3%
105 1
 
0.3%
107.5 1
 
0.3%
109 2
0.5%
110 1
 
0.3%
111 1
 
0.3%
113 1
 
0.3%
114 1
 
0.3%
115 4
1.0%
ValueCountFrequency (%)
322 1
0.3%
317 1
0.3%
308 1
0.3%
285.5 1
0.3%
283.5 1
0.3%
280.5 1
0.3%
279.5 1
0.3%
277 1
0.3%
275 1
0.3%
272.5 1
0.3%

BMI
Real number (ℝ)

HIGH CORRELATION 

Distinct356
Distinct (%)92.0%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean28.597051
Minimum16.00193
Maximum55.26514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:17.116884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16.00193
5-th percentile19.539222
Q124.132565
median27.64079
Q331.93694
95-th percentile40.612259
Maximum55.26514
Range39.26321
Interquartile range (IQR)7.804375

Descriptive statistics

Standard deviation6.4398836
Coefficient of variation (CV)0.22519397
Kurtosis0.93633767
Mean28.597051
Median Absolute Deviation (MAD)3.84694
Skewness0.8416397
Sum11067.059
Variance41.472101
MonotonicityNot monotonic
2024-04-12T18:11:17.420443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.61444 3
 
0.8%
30.69459 3
 
0.8%
29.38395 3
 
0.8%
27.17097 3
 
0.8%
21.43185 2
 
0.5%
29.0783 2
 
0.5%
28.18891 2
 
0.5%
27.24125 2
 
0.5%
34.95163 2
 
0.5%
25.05811 2
 
0.5%
Other values (346) 363
93.1%
(Missing) 3
 
0.8%
ValueCountFrequency (%)
16.00193 1
0.3%
16.34353 1
0.3%
17.21633 1
0.3%
17.3598 1
0.3%
17.91391 1
0.3%
17.93431 1
0.3%
18.02124 1
0.3%
18.13657 1
0.3%
18.55946 1
0.3%
18.7926 1
0.3%
ValueCountFrequency (%)
55.26514 1
0.3%
50.56853 1
0.3%
49.50579 1
0.3%
48.61723 1
0.3%
47.13746 1
0.3%
45.47868 1
0.3%
45.07773 1
0.3%
44.30321 1
0.3%
44.2077 1
0.3%
43.39506 1
0.3%

Systolic_Blood_Pressure
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)18.4%
Missing5
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean137.14805
Minimum90
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:17.720901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile106.4
Q1121
median136
Q3148
95-th percentile179.8
Maximum250
Range160
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.997427
Coefficient of variation (CV)0.16768322
Kurtosis2.3299738
Mean137.14805
Median Absolute Deviation (MAD)14
Skewness1.0913893
Sum52802
Variance528.88167
MonotonicityNot monotonic
2024-04-12T18:11:18.018291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140 35
 
9.0%
130 30
 
7.7%
110 26
 
6.7%
138 19
 
4.9%
120 18
 
4.6%
150 17
 
4.4%
142 16
 
4.1%
122 13
 
3.3%
136 13
 
3.3%
118 12
 
3.1%
Other values (61) 186
47.7%
ValueCountFrequency (%)
90 1
 
0.3%
98 1
 
0.3%
100 7
 
1.8%
102 2
 
0.5%
103 1
 
0.3%
104 3
 
0.8%
105 2
 
0.5%
106 3
 
0.8%
108 7
 
1.8%
110 26
6.7%
ValueCountFrequency (%)
250 1
 
0.3%
230 1
 
0.3%
220 1
 
0.3%
218 1
 
0.3%
212 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
190 5
1.3%
186 1
 
0.3%
184 1
 
0.3%

Diastolic_Blood_Pressure
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)14.5%
Missing5
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean83.285714
Minimum48
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:18.331569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q175
median82
Q390
95-th percentile109.6
Maximum124
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.582366
Coefficient of variation (CV)0.16308158
Kurtosis0.077513885
Mean83.285714
Median Absolute Deviation (MAD)8
Skewness0.24526787
Sum32065
Variance184.48065
MonotonicityNot monotonic
2024-04-12T18:11:18.633537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 37
 
9.5%
80 28
 
7.2%
78 22
 
5.6%
82 21
 
5.4%
70 17
 
4.4%
86 16
 
4.1%
100 16
 
4.1%
88 15
 
3.8%
75 14
 
3.6%
72 14
 
3.6%
Other values (46) 185
47.4%
ValueCountFrequency (%)
48 1
 
0.3%
50 2
 
0.5%
52 1
 
0.3%
53 1
 
0.3%
56 1
 
0.3%
58 3
0.8%
59 1
 
0.3%
60 6
1.5%
61 1
 
0.3%
62 5
1.3%
ValueCountFrequency (%)
124 1
 
0.3%
122 1
 
0.3%
120 2
 
0.5%
118 2
 
0.5%
115 2
 
0.5%
114 1
 
0.3%
112 2
 
0.5%
110 9
2.3%
108 1
 
0.3%
106 1
 
0.3%

waist
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)7.7%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean37.896907
Minimum26
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:18.918125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29
Q133
median37
Q341
95-th percentile48
Maximum56
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.7627235
Coefficient of variation (CV)0.15206316
Kurtosis-0.15563905
Mean37.896907
Median Absolute Deviation (MAD)4
Skewness0.46717168
Sum14704
Variance33.208983
MonotonicityNot monotonic
2024-04-12T18:11:19.164776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37 31
 
7.9%
40 27
 
6.9%
38 27
 
6.9%
33 26
 
6.7%
36 25
 
6.4%
34 23
 
5.9%
39 23
 
5.9%
32 21
 
5.4%
31 21
 
5.4%
35 19
 
4.9%
Other values (20) 145
37.2%
ValueCountFrequency (%)
26 2
 
0.5%
27 1
 
0.3%
28 7
 
1.8%
29 11
2.8%
30 10
 
2.6%
31 21
5.4%
32 21
5.4%
33 26
6.7%
34 23
5.9%
35 19
4.9%
ValueCountFrequency (%)
56 1
 
0.3%
55 1
 
0.3%
53 2
 
0.5%
52 2
 
0.5%
51 4
1.0%
50 3
 
0.8%
49 6
1.5%
48 8
2.1%
47 7
1.8%
46 9
2.3%

hip
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)8.2%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean43.033505
Minimum30
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-12T18:11:19.447788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35
Q139
median42
Q346
95-th percentile54
Maximum64
Range34
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6492126
Coefficient of variation (CV)0.13127475
Kurtosis0.87799803
Mean43.033505
Median Absolute Deviation (MAD)3
Skewness0.79830729
Sum16697
Variance31.913603
MonotonicityNot monotonic
2024-04-12T18:11:19.707288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
39 35
 
9.0%
41 35
 
9.0%
40 33
 
8.5%
38 28
 
7.2%
43 28
 
7.2%
42 27
 
6.9%
47 22
 
5.6%
44 21
 
5.4%
46 20
 
5.1%
45 20
 
5.1%
Other values (22) 119
30.5%
ValueCountFrequency (%)
30 1
 
0.3%
32 1
 
0.3%
33 8
 
2.1%
34 5
 
1.3%
35 11
 
2.8%
36 6
 
1.5%
37 15
3.8%
38 28
7.2%
39 35
9.0%
40 33
8.5%
ValueCountFrequency (%)
64 1
 
0.3%
62 2
 
0.5%
60 1
 
0.3%
59 1
 
0.3%
58 5
1.3%
57 2
 
0.5%
56 1
 
0.3%
55 2
 
0.5%
54 6
1.5%
53 5
1.3%

CLASS
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size650.0 B
1
298 
3
67 
2
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters390
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 298
76.4%
3 67
 
17.2%
2 25
 
6.4%

Length

2024-04-12T18:11:19.981076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-12T18:11:20.236753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 298
76.4%
3 67
 
17.2%
2 25
 
6.4%

Most occurring characters

ValueCountFrequency (%)
1 298
76.4%
3 67
 
17.2%
2 25
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 298
76.4%
3 67
 
17.2%
2 25
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 298
76.4%
3 67
 
17.2%
2 25
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 298
76.4%
3 67
 
17.2%
2 25
 
6.4%

Interactions

2024-04-12T18:11:05.243372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:22.153874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:26.440356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:29.663560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:32.742989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:37.074934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:40.274734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:44.316569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:47.887795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:51.398733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:54.631589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:57.892207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:01.792816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:05.488240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:22.511058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:26.692077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:29.912847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:33.081808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:37.324008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:40.527783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:44.544723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:48.209981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:51.646181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:54.887494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:58.155352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:02.156245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:05.754276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:22.855767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:26.942402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:30.153892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:33.486932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:37.583908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:40.791053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:44.788497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:48.510245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:51.901813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:55.141065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:58.428750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:02.547738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:05.991115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:23.879349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:27.201418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:30.387876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:33.857092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:37.814883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:41.026236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:45.034852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:48.813210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:52.159855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:55.389036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:58.647563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:02.807357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:06.245361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:24.149975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:27.456169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:30.620601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:34.253556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:38.062813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:41.278387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:45.272580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:49.190898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:52.412911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:55.645084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:58.891900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:03.063664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:07.431363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:24.392366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:27.690209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:30.853125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:34.581650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:38.300982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:41.524073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:45.500387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:49.498142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:52.647572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:55.887774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:59.132200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:03.313725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:07.673265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:24.639435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:27.936526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:31.092833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:34.936780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:38.544134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:41.783521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:45.730051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:49.744849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:52.909584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:56.134810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:59.389033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:03.558100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:07.914321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:24.862142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:28.179656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:31.324554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:35.286702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:38.786889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:42.013349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:45.964204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:49.984913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:53.136726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:56.383913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:59.709190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:03.794738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:08.131788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:25.120534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:28.402498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:31.541734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:35.606121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:39.030390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:42.249539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:46.262513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:50.204446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:53.377902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:56.615719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:00.070824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:04.007417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:08.383729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:25.382891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:28.655433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:31.795125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:35.999651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:39.286922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:43.293791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:46.642840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:50.456495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:53.632494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:56.870088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:00.413620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:04.254851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:08.630388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:25.665886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:28.898322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:32.030475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:36.268250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:39.526200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:43.547074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:46.991647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:50.682597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:53.887320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:57.119799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:00.719313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:04.513357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:08.890978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:25.940761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:29.165064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:32.274152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:36.542784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:39.785007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:43.810994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:47.296706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:50.921910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:54.143698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:57.393732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:01.101415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:04.777951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:09.126065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:26.190647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:29.420740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:32.510987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:36.800344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:40.023633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:44.050824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:47.609563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:51.166451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:54.387829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:10:57.639324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:01.459705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-12T18:11:05.005346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-12T18:11:20.469715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeBMICLASSDiastolic_Blood_PressureGenderHeightSystolic_Blood_PressureWeightcholglyhbhdlhipratiostab.gluwaist
Age1.0000.0140.2690.0840.071-0.0670.467-0.0070.2830.430-0.0510.0360.1910.3390.166
BMI0.0141.0000.0340.1950.262-0.2480.1600.8350.1290.211-0.2440.8780.3150.1840.806
CLASS0.2690.0341.0000.0220.0000.0510.2490.1670.1400.734-0.1800.1580.2240.5250.269
Diastolic_Blood_Pressure0.0840.1950.0221.0000.0150.0560.5900.1850.1830.0530.0570.1880.0370.0860.193
Gender0.0710.2620.0000.0151.0000.7090.0700.101-0.0280.062-0.135-0.2820.1110.037-0.047
Height-0.067-0.2480.0510.0560.7091.0000.0120.278-0.1180.036-0.140-0.1150.0650.0280.061
Systolic_Blood_Pressure0.4670.1600.2490.5900.0700.0121.0000.1490.2080.285-0.0300.1770.1210.2720.227
Weight-0.0070.8350.1670.1850.1010.2780.1491.0000.0650.229-0.3240.8070.3540.2060.839
chol0.2830.1290.1400.183-0.028-0.1180.2080.0651.0000.2290.1460.1160.4000.1350.117
glyhb0.4300.2110.7340.0530.0620.0360.2850.2290.2291.000-0.1920.2250.2920.5310.313
hdl-0.051-0.244-0.1800.057-0.135-0.140-0.030-0.3240.146-0.1921.000-0.219-0.813-0.202-0.307
hip0.0360.8780.1580.188-0.282-0.1150.1770.8070.1160.225-0.2191.0000.2950.2000.833
ratio0.1910.3150.2240.0370.1110.0650.1210.3540.4000.292-0.8130.2951.0000.2480.371
stab.glu0.3390.1840.5250.0860.0370.0280.2720.2060.1350.531-0.2020.2000.2481.0000.246
waist0.1660.8060.2690.193-0.0470.0610.2270.8390.1170.313-0.3070.8330.3710.2461.000

Missing values

2024-04-12T18:11:09.494354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-12T18:11:10.027572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-12T18:11:10.424046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

cholstab.gluhdlratioglyhbAgeGenderHeightWeightBMISystolic_Blood_PressureDiastolic_Blood_PressurewaisthipCLASS
0203.08256.03.64.3146female62.0120.021.94589118.059.029.038.01
1165.09724.06.94.4429female64.0218.037.41553112.068.046.048.01
2228.09237.06.24.6458female61.0249.547.13746190.092.049.057.01
378.09312.06.54.6367male67.0120.018.79260110.050.033.038.01
4249.09028.08.97.7264male68.0181.027.51795138.080.044.041.03
5248.09469.03.64.8134male71.0188.026.21781132.086.036.042.01
6195.09241.04.84.8430male69.0185.527.39057161.0112.046.049.01
7227.07544.05.23.9437male59.0170.034.33209NaNNaN34.039.01
8177.08749.03.64.8445male69.0166.024.51124160.080.034.040.01
9263.08940.06.65.7855female63.0200.035.42454108.072.045.050.03
cholstab.gluhdlratioglyhbAgeGenderHeightWeightBMISystolic_Blood_PressureDiastolic_Blood_PressurewaisthipCLASS
380221.012648.04.65.53000059female62.0173.531.73010130.078.039.045.01
381210.08181.02.64.96000078male66.0145.023.40106110.070.038.039.01
382192.08569.02.84.38000051male65.0143.023.79385130.0110.0NaNNaN1
383169.010458.02.94.82000025female60.0150.029.29167140.095.040.042.01
384179.08550.03.64.99000037male66.0137.522.19066190.094.033.039.01
385301.090118.02.64.28000089female61.0118.022.29347218.090.031.041.01
386296.036946.06.416.11000153male69.0181.026.72611138.094.035.039.03
387284.08954.05.34.39000051female63.0154.027.27690140.0100.032.043.01
388194.026938.05.113.63000029female69.0168.524.88038120.070.033.040.03
389199.07652.03.84.49000041female63.0193.034.18468120.078.041.048.01